docs: def random_rescaling_factor_to_mahalanobis_size(self, y):
          """``self.mean + self.random_rescaling_factor_to_mahalanobis_size(y)``
          is guarantied to appear like from the sample distribution.
          """
          if len(y) != self.N:
              raise ValueError('len(y)=%d != %d=dimension' % (len(y), self.N))
          if not any(y):
              _print_warning("input was all-zeros, which is probably a bug",
                             "random_rescaling_factor_to_mahalanobis_size",
                             iteration=self.countiter)
              return 1.0
          return sum(self.randn(len(y))**2)**0.5 / self.mahalanobis_norm(y)